{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph Ensembles for node attribute inference\n",
    "\n",
    "This notebook demonstrates the use of `stellargraph`'s `Ensemble` class for node attribute inference using the Cora and Pubmed-Diabetes citation datasets.\n",
    "\n",
    "The `Ensemble` class brings ensemble learning to `stellargraph`'s graph neural network models, e.g., `GraphSAGE` and `GCN`, quantifying prediction variance and potentially improving prediction accuracy. \n",
    "\n",
    "**References**\n",
    "\n",
    "1. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 \n",
    "[cs.SI], 2017.\n",
    "\n",
    "\n",
    "2. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. ICLR 2017. arXiv:1609.02907 \n",
    "\n",
    "\n",
    "3. Graph Attention Networks. P. Velickovic et al. ICLR 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import GraphSAGENodeGenerator, FullBatchNodeGenerator\n",
    "from stellargraph.layer import GraphSAGE, GCN, GAT\n",
    "from stellargraph import globalvar\n",
    "\n",
    "from stellargraph import Ensemble, BaggingEnsemble\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, models\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    def remove_prefix(text, prefix):\n",
    "        return text[text.startswith(prefix) and len(prefix) :]\n",
    "\n",
    "    figsize = (6, 4)\n",
    "    c_train = \"b\"\n",
    "    c_test = \"g\"\n",
    "\n",
    "    metrics = sorted(\n",
    "        set([remove_prefix(m, \"val_\") for m in list(history[0].history.keys())])\n",
    "    )\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.figure(figsize=figsize)\n",
    "        for h in history:\n",
    "            plt.plot(h.history[m], c=c_train)\n",
    "            plt.plot(h.history[\"val_\" + m], c=c_test)\n",
    "\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel(\"epoch\")\n",
    "        plt.legend([\"train\", \"validation\"], loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the network data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "The PubMed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(datasets.Cora().description))\n",
    "display(HTML(datasets.PubMedDiabetes().description))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we select the dataset to use, either Cora or Pubmed-Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cora = True  # Select the dataset; if False, then Pubmed-Diabetes dataset is used.\n",
    "if use_cora:\n",
    "    dataset = datasets.Cora()\n",
    "else:\n",
    "    dataset = datasets.PubMedDiabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_cora(data_dir, largest_cc=False):\n",
    "    g_nx = nx.read_edgelist(path=os.path.expanduser(os.path.join(data_dir, \"cora.cites\")))\n",
    "\n",
    "    for edge in g_nx.edges(data=True):\n",
    "        edge[2][\"label\"] = \"cites\"\n",
    "\n",
    "    # load the node attribute data\n",
    "    cora_data_location = os.path.expanduser(os.path.join(data_dir, \"cora.content\"))\n",
    "    node_attr = pd.read_csv(cora_data_location, sep=\"\\t\", header=None)\n",
    "    values = {str(row.tolist()[0]): row.tolist()[-1] for _, row in node_attr.iterrows()}\n",
    "    nx.set_node_attributes(g_nx, values, \"subject\")\n",
    "\n",
    "    if largest_cc:\n",
    "        # Select the largest connected component. For clarity we ignore isolated\n",
    "        # nodes and subgraphs; having these in the data does not prevent the\n",
    "        # algorithm from running and producing valid results.\n",
    "        g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
    "        g_nx = max(g_nx_ccs, key=len)\n",
    "        print(\n",
    "            \"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
    "                g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "            )\n",
    "        )\n",
    "\n",
    "    feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "    column_names = feature_names + [\"subject\"]\n",
    "    node_data = pd.read_csv(\n",
    "        os.path.join(data_dir, \"cora.content\"), sep=\"\\t\", header=None, names=column_names\n",
    "    )\n",
    "\n",
    "    node_data.index = node_data.index.map(str)\n",
    "    node_data = node_data[node_data.index.isin(list(g_nx.nodes()))]\n",
    "\n",
    "    for nid in node_data.index:\n",
    "        g_nx.nodes[nid][globalvar.TYPE_ATTR_NAME] = \"paper\"  # specify node type\n",
    "\n",
    "    return g_nx, node_data, feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pubmed(data_dir):\n",
    "    edgelist = pd.read_csv(\n",
    "        os.path.join(data_dir, \"Pubmed-Diabetes.DIRECTED.cites.tab\"),\n",
    "        sep=\"\\t\",\n",
    "        skiprows=2,\n",
    "        header=None,\n",
    "    )\n",
    "    edgelist.drop(columns=[0, 2], inplace=True)\n",
    "    edgelist.columns = [\"source\", \"target\"]\n",
    "    # delete unneccessary prefix\n",
    "    edgelist[\"source\"] = edgelist[\"source\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"target\"] = edgelist[\"target\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"label\"] = \"cites\"  # set the edge type\n",
    "\n",
    "    # Load the graph from the edgelist\n",
    "    g_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")\n",
    "\n",
    "    # Load the features and subject for each node in the graph\n",
    "    nodes_as_dict = []\n",
    "    with open(\n",
    "        os.path.join(os.path.expanduser(data_dir), \"Pubmed-Diabetes.NODE.paper.tab\")\n",
    "    ) as fp:\n",
    "        for line in itertools.islice(fp, 2, None):\n",
    "            line_res = line.split(\"\\t\")\n",
    "            pid = line_res[0]\n",
    "            feat_name = [\"pid\"] + [l.split(\"=\")[0] for l in line_res[1:]][\n",
    "                :-1\n",
    "            ]  # delete summary\n",
    "            feat_value = [l.split(\"=\")[1] for l in line_res[1:]][:-1]  # delete summary\n",
    "            feat_value = [pid] + [\n",
    "                float(x) for x in feat_value\n",
    "            ]  # change to numeric from str\n",
    "            row = dict(zip(feat_name, feat_value))\n",
    "            nodes_as_dict.append(row)\n",
    "\n",
    "    # Create a Pandas dataframe holding the node data\n",
    "    node_data = pd.DataFrame(nodes_as_dict)\n",
    "    node_data.fillna(0, inplace=True)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(int)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(str)\n",
    "\n",
    "    node_data.index = node_data[\"pid\"]\n",
    "    node_data.drop(columns=[\"pid\"], inplace=True)\n",
    "    node_data.head()\n",
    "\n",
    "    for nid in node_data.index:\n",
    "        g_nx.nodes[nid][globalvar.TYPE_ATTR_NAME] = \"paper\"  # specify node type\n",
    "\n",
    "    feature_names = list(node_data.columns)\n",
    "    feature_names.remove(\"label\")\n",
    "\n",
    "    return g_nx, node_data, feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_cora:\n",
    "    Gnx, node_data, feature_names = load_cora(dataset.data_directory)\n",
    "else:\n",
    "    Gnx, node_data, feature_names = load_pubmed(dataset.data_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" or \"label\" attribute on the nodes depending on the selected dataset. These subjects are one of 7 or 3 categories for Cora and PubMed-Diabetes respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Probabilistic_Methods', 'Reinforcement_Learning', 'Genetic_Algorithms', 'Theory', 'Neural_Networks', 'Rule_Learning', 'Case_Based'}\n"
     ]
    }
   ],
   "source": [
    "# Print the class names for the selected dataset\n",
    "if use_cora:\n",
    "    print(set(node_data[\"subject\"]))\n",
    "else:\n",
    "    print(set(node_data[\"label\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_label = \"label\"\n",
    "if use_cora:\n",
    "    node_label = \"subject\"\n",
    "\n",
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data,\n",
    "    train_size=0.2,  # 140\n",
    "    test_size=None,\n",
    "    stratify=node_data[node_label],\n",
    "    random_state=42,\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data,\n",
    "    train_size=0.2,  # 500,\n",
    "    test_size=None,\n",
    "    stratify=test_data[node_label],\n",
    "    random_state=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[node_label]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[node_label]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[node_label]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify global parameters\n",
    "\n",
    "Here we specify some parameters that control the type of model we are going to use. For example, we specify the base model type, e.g., GCN, GraphSAGE, etc, and the number of estimators in the ensemble as well as model-specific parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_type = \"graphsage\"  # Can be either gcn, gat, or graphsage\n",
    "use_bagging = (\n",
    "    True  # If True, each model in the ensemble is trained on a bootstrapped sample\n",
    ")\n",
    "# of the given training data; otherwise, the same training data are used\n",
    "# for training each model.\n",
    "\n",
    "if model_type == \"graphsage\":\n",
    "    # For GraphSAGE model\n",
    "    batch_size = 50\n",
    "    num_samples = [10, 10]\n",
    "    n_estimators = 5  # The number of estimators in the ensemble\n",
    "    n_predictions = 10  # The number of predictions per estimator per query point\n",
    "    epochs = 50  # The number of training epochs\n",
    "elif model_type == \"gcn\":\n",
    "    # For GCN model\n",
    "    n_estimators = 5  # The number of estimators in the ensemble\n",
    "    n_predictions = 10  # The number of predictions per estimator per query point\n",
    "    epochs = 50  # The number of training epochs\n",
    "elif model_type == \"gat\":\n",
    "    # For GAT model\n",
    "    layer_sizes = [8, train_targets.shape[1]]\n",
    "    attention_heads = 8\n",
    "    n_estimators = 5  # The number of estimators in the ensemble\n",
    "    n_predictions = 10  # The number of predictions per estimator per query point\n",
    "    epochs = 200  # The number of training epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the base graph machine learning model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a `StellarGraph` object from the `NetworkX` graph and the node features and targets. It is `StellarGraph` objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "        Attributes: {'subject'}\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator that feeds data from the graph into the model. The generators are specialized to the model and the learning task. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training for those generators that support shuffling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == \"graphsage\":\n",
    "    generator = GraphSAGENodeGenerator(G, batch_size, num_samples)\n",
    "    train_gen = generator.flow(train_data.index, train_targets, shuffle=True)\n",
    "elif model_type == \"gcn\":\n",
    "    generator = FullBatchNodeGenerator(G, method=\"gcn\")\n",
    "    train_gen = generator.flow(\n",
    "        train_data.index, train_targets\n",
    "    )  # does not support shuffle\n",
    "elif model_type == \"gat\":\n",
    "    generator = FullBatchNodeGenerator(G, method=\"gat\")\n",
    "    train_gen = generator.flow(\n",
    "        train_data.index, train_targets\n",
    "    )  # does not support shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "541"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this but the parameters are model-specific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == \"graphsage\":\n",
    "    base_model = GraphSAGE(\n",
    "        layer_sizes=[16, 16], generator=generator, bias=True, dropout=0.5, normalize=\"l2\"\n",
    "    )\n",
    "    x_inp, x_out = base_model.build()\n",
    "    prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)\n",
    "elif model_type == \"gcn\":\n",
    "    base_model = GCN(\n",
    "        layer_sizes=[32, train_targets.shape[1]],\n",
    "        generator=generator,\n",
    "        bias=True,\n",
    "        dropout=0.5,\n",
    "        activations=[\"elu\", \"softmax\"],\n",
    "    )\n",
    "    x_inp, x_out = base_model.build()\n",
    "    prediction = x_out\n",
    "elif model_type == \"gat\":\n",
    "    base_model = GAT(\n",
    "        layer_sizes=layer_sizes,\n",
    "        attn_heads=attention_heads,\n",
    "        generator=generator,\n",
    "        bias=True,\n",
    "        in_dropout=0.5,\n",
    "        attn_dropout=0.5,\n",
    "        activations=[\"elu\", \"softmax\"],\n",
    "    )\n",
    "    x_inp, prediction = base_model.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the shape of the output tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, 7])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Keras model and then an Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `base_model` and outputs being the predictions from the softmax layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the ensemble model consisting of `n_estimators` models.\n",
    "\n",
    "We are also going to specify that we want to make `n_predictions` per query point per model. These predictions will differ because of the application of `dropout` and, in the case of ensembling GraphSAGE models, the sampling of node neighbourhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_bagging:\n",
    "    model = BaggingEnsemble(model, n_estimators=n_estimators, n_predictions=n_predictions)\n",
    "else:\n",
    "    model = Ensemble(model, n_estimators=n_estimators, n_predictions=n_predictions)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<stellargraph.utils.ensemble.BaggingEnsemble at 0x14967b6d8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model is of type stellargraph.utils.ensemble.Ensemble but has\n",
    "# a very similar interface to a Keras model\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble has `n_estimators` models. Let's have a look at the first model's layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x141a77390>,\n",
       " <tensorflow.python.keras.engine.input_layer.InputLayer at 0x149681d30>,\n",
       " <tensorflow.python.keras.engine.input_layer.InputLayer at 0x149681668>,\n",
       " <tensorflow.python.keras.layers.core.Reshape at 0x14c309550>,\n",
       " <tensorflow.python.keras.layers.core.Reshape at 0x14c309a58>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x14c330780>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x1496816a0>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x14c4bbb00>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x14c41cf60>,\n",
       " <stellargraph.layer.graphsage.MeanAggregator at 0x149681128>,\n",
       " <tensorflow.python.keras.layers.core.Reshape at 0x14c430dd8>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x14c4e0898>,\n",
       " <tensorflow.python.keras.layers.core.Dropout at 0x14c50eef0>,\n",
       " <stellargraph.layer.graphsage.MeanAggregator at 0x149681400>,\n",
       " <tensorflow.python.keras.layers.core.Reshape at 0x14c47ce10>,\n",
       " <tensorflow.python.keras.layers.core.Lambda at 0x14967bd68>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x14c5e7cf8>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its performance on the validation set during the training (e.g., for early stopping), and generalization performance of the final model on a held-out test set (we need to create another generator over the test data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)\n",
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the amount of time to train the ensemble is linear to `n_estimators`.\n",
    "\n",
    "Also, we are going to use early stopping by monitoring the accuracy on the validation data and stopping if the accuracy does not increase after 10 training epochs (this is the default grace value specified by the `Ensemble` class but we can set it to a different value by using `model.early_stopping_patience=20` for example.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_bagging:\n",
    "    # When using bootstrap samples to train each model in the ensemble, we must specify\n",
    "    # the IDs of the training nodes (train_data) and their corresponding target values\n",
    "    # (train_targets)\n",
    "    history = model.fit_generator(\n",
    "        generator,\n",
    "        train_data=train_data.index,\n",
    "        train_targets=train_targets,\n",
    "        epochs=epochs,\n",
    "        validation_data=val_gen,\n",
    "        verbose=0,\n",
    "        shuffle=False,\n",
    "        bag_size=None,\n",
    "        use_early_stopping=True,  # Enable early stopping\n",
    "        early_stopping_monitor=\"val_acc\",\n",
    "    )\n",
    "else:\n",
    "    history = model.fit_generator(\n",
    "        train_gen,\n",
    "        epochs=epochs,\n",
    "        validation_data=val_gen,\n",
    "        verbose=0,\n",
    "        shuffle=False,\n",
    "        use_early_stopping=True,  # Enable early stopping\n",
    "        early_stopping_monitor=\"val_acc\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model, let's evaluate it on the test set. Note that the `.evaluate_generator()` method of the `Ensemble` class returns mean and standard deviation of each evaluation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics of the trained models:\n",
      "\tloss: 0.7295±0.0700\n",
      "\tacc: 0.8031±0.0099\n"
     ]
    }
   ],
   "source": [
    "test_metrics_mean, test_metrics_std = model.evaluate_generator(test_gen)\n",
    "\n",
    "print(\"\\nTest Set Metrics of the trained models:\")\n",
    "for name, m, s in zip(model.metrics_names, test_metrics_mean, test_metrics_std):\n",
    "    print(\"\\t{}: {:0.4f}±{:0.4f}\".format(name, m, s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes, using a new generator for all nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_gen = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(generator=all_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10, 2708, 7)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full-batch methods, the batch dimension is 1 so we will remove any singleton dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions = np.squeeze(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10, 2708, 7)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For demonstration, we are going to select one of the nodes in the graph, and plot the ensemble's predictions for that node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_query_point = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The array `all_predictions` has dimensionality $MxKxNxF$ where $M$ is the number of estimators in the ensemble (`n_estimators`); $K$ is the number of predictions per query point per estimator (`n_predictions`); $N$ is the number of query points (`len(all_predictions)`); and $F$ is the output dimensionality of the specified layer determined by the shape of the output layer.\n",
    "\n",
    "Since we are only interested in the predictions for a single query node, e.g., `selected_query_point`, we are going to slice the array to extract them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10, 7)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the predictions for the point specified by selected_query_point\n",
    "qp_predictions = all_predictions[:, :, selected_query_point, :]\n",
    "# The shape should be n_estimators x n_predictions x size_output_layer\n",
    "qp_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, to facilitate plotting the predictions using either a density plot or a box plot, we are going to reshape `qp_predictions` to $R\\times F$ where $R$ is equal to $M\\times K$ as above and $F$ is the output dimensionality of the output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 7)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qp_predictions = qp_predictions.reshape(\n",
    "    np.product(qp_predictions.shape[0:-1]), qp_predictions.shape[-1]\n",
    ")\n",
    "qp_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'subject=Case_Based',\n",
       " 1: 'subject=Genetic_Algorithms',\n",
       " 2: 'subject=Neural_Networks',\n",
       " 3: 'subject=Probabilistic_Methods',\n",
       " 4: 'subject=Reinforcement_Learning',\n",
       " 5: 'subject=Rule_Learning',\n",
       " 6: 'subject=Theory'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_subject_mapper = {k: v for k, v in enumerate(target_encoding.feature_names_)}\n",
    "inv_subject_mapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to assess the ensemble's confidence in its predictions in order to decide if we can trust them or not. Utilising density plots, we can visually inspect the ensemble's distribution of prediction probabilities for a node's label.\n",
    "\n",
    "This is better demonstrated if the ensemble's base mode is `GraphSAGE` because the predictions of the base model vary most (when compared to GCN and GAT) due to the random sampling of node neighbours during prediction in addition to the inherent stocasticity of the ensemble itself.\n",
    "\n",
    "If the density plot for the predicted node label is well separated from those of the other labels with little overlap then we can be confident trusting the model's prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if model_type not in [\"gcn\", \"gat\"]:\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    for i in range(qp_predictions.shape[1]):\n",
    "        sns.kdeplot(data=qp_predictions[:, i].reshape((-1,)), label=inv_subject_mapper[i])\n",
    "    plt.xlabel(\"Predicted Probability\")\n",
    "    plt.title(\"Density plots of predicted probabilities for each subject\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative and possibly more informative view of the distribution of node predictions is a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Subject')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "ax.boxplot(x=qp_predictions)\n",
    "ax.set_xticklabels(target_encoding.feature_names_)\n",
    "ax.tick_params(axis=\"x\", rotation=45)\n",
    "if model_type == \"graphsage\":\n",
    "    y = np.argmax(\n",
    "        target_encoding.transform(node_data[[node_label]].to_dict(\"records\")), axis=1\n",
    "    )\n",
    "elif model_type == \"gcn\" or model_type == \"gat\":\n",
    "    y = np.argmax(\n",
    "        target_encoding.transform(\n",
    "            node_data.reindex(G.nodes())[[node_label]].to_dict(\"records\")\n",
    "        ),\n",
    "        axis=1,\n",
    "    )\n",
    "plt.title(\"Correct \" + target_encoding.feature_names_[y[selected_query_point]])\n",
    "plt.ylabel(\"Predicted Probability\")\n",
    "plt.xlabel(\"Subject\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above example shows that the ensemble predicts the correct node label with high confidence so we can trust its prediction.\n",
    "\n",
    "(Note that due to the stochastic nature of training neural network algorithms, the above conclusion may not be valid if you re-run the notebook; however, the general conclusion that the use of ensemble learning can be used to quantify the model's uncertainty about its predictions still holds.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "\n",
    "Evaluate node embeddings as activations of the output of one of the graph convolutional or aggregation layers in the ensemble model, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "You can find the index of the layer of interest by calling the `Ensemble` class's method `layers`, e.g., `model.layers()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == \"graphsage\":\n",
    "    # For GraphSAGE, we are going to use the output activations of the second GraphSAGE layer\n",
    "    # as the node embeddings\n",
    "    emb = model.predict_generator(\n",
    "        generator=generator, predict_data=node_data.index, output_layer=-4\n",
    "    )  # this selects the output layer\n",
    "elif model_type == \"gcn\" or model_type == \"gat\":\n",
    "    # For GCN and GAT, we are going to use the output activations of the first GCN or Graph\n",
    "    # Attention layer as the node embeddings\n",
    "    emb = model.predict_generator(\n",
    "        generator=generator, predict_data=node_data.index, output_layer=6\n",
    "    )  # this selects the output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The array `emb` has dimensionality $MxKxNxF$ (or $MxKx1xNxF$for full batch methods) where $M$ is the number of estimators in the ensemble (`n_estimators`); $K$ is the number of predictions per query point per estimator (`n_predictions`); $N$ is the number of query points (`len(node_data.index)`); and $F$ is the output dimensionality of the specified layer determined by the shape of the readout layer as specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10, 2708, 1, 16)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10, 2708, 16)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = np.squeeze(emb)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we are going to average the predictions over the number of models and the number of predictions per query point. \n",
    "\n",
    "The dimensionality of the array will then be **NxF** where N is the number of points to predict (equal to the number of nodes in the graph for this example) and F is the dimensionality of the embeddings that depends on the output shape of the readout layer as specified above.\n",
    "\n",
    "Note that we could have achieved the same by specifying `summarise=True` in the call to the method `predict_generator` above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = np.mean(emb, axis=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 16)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[node_label]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_data.index)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of {} embeddings for cora dataset\".format(\n",
    "        model_type, transform.__name__\n",
    "    )\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
